Brain Tumor Segmentation using 3D U-Net with Hyperparameter Optimization

Gamal, Ali A. El; Bedda, Khaled; Ashraf, Nada; Ayman, Salma; Abdallah, Mohamed Hosny; Rushdi, Muhammad A.

doi:10.1109/niles53778.2021.9600556

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…When using 3D segmentation, less annotated images are required due to the redundancy resulting from the repeating structures and shapes within the volume channels, therefore, faster training with scarcely annotated data is efficient. After 3D U-Net was proposed, the majority of research adopted it extensively with 3D volumetric CT scans and MR image segmentation for two main applications, with the first being diagnosing diseases such as cardiac structures [28], brain tumors [29][30][31], liver tumors [32,33], and bone structures [34]. Moreover, many other applications fall into the two preceding mentioned fields.…”

Section: D U-netmentioning

confidence: 99%

U-Net-Based Models towards Optimal MR Brain Image Segmentation

et al. 2023

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Brain tumor segmentation from MRIs has always been a challenging task for radiologists, therefore, an automatic and generalized system to address this task is needed. Among all other deep learning techniques used in medical imaging, U-Net-based variants are the most used models found in the literature to segment medical images with respect to different modalities. Therefore, the goal of this paper is to examine the numerous advancements and innovations in the U-Net architecture, as well as recent trends, with the aim of highlighting the ongoing potential of U-Net being used to better the performance of brain tumor segmentation. Furthermore, we provide a quantitative comparison of different U-Net architectures to highlight the performance and the evolution of this network from an optimization perspective. In addition to that, we have experimented with four U-Net architectures (3D U-Net, Attention U-Net, R2 Attention U-Net, and modified 3D U-Net) on the BraTS 2020 dataset for brain tumor segmentation to provide a better overview of this architecture’s performance in terms of Dice score and Hausdorff distance 95%. Finally, we analyze the limitations and challenges of medical image analysis to provide a critical discussion about the importance of developing new architectures in terms of optimization.

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Section: D U-netmentioning

confidence: 99%

U-Net-Based Models towards Optimal MR Brain Image Segmentation

et al. 2023

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“…In the context of training the Attention 3D-CU-Net for kidney tumor segmentation, hyperparameter tuning [38] played a crucial role in optimizing the model's performance. This section outlines the hyperparameters that were systematically tuned, the methodology employed for tuning, and the final chosen values that yielded the best results.…”

Section: ) Hyperparameter Tuningmentioning

confidence: 99%

Attention 3D-CU-Net: Enhancing Kidney Tumor Segmentation Accuracy Through Selective Feature Emphasis

Sitanaboina,

Beeram,

Jonnadula

et al. 2023

IEEE Access

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Kidney tumor segmentation from MR images became a pivotal research area in kidney cancer diagnosis and treatment planning. Accurate and efficient segmentation enables precise tumor localization, treatment planning, and monitoring of disease progression. Former studies have demonstrated the remarkable capability of the U-Net architecture in semantic segmentation of kidney tumors. A recent variant of the U-Net architecture, known as 3D-CU-Net, has been specifically designed with fully-connected dense skip connections to tackle the kidney tumor segmentation challenges related to network depth invariance, segmentation errors, and enforced feature fusion. While the 3D-CU-Net model demonstrated improved effectiveness in kidney tumor segmentation, it still exhibits significant limitations, including challenges in precise localization, fixed feature selection, image diversity, limited contextual information, and computational complexity. To address the limitations of the 3D-CU-Net model, this paper introduces the Attention 3D-CU-Net as a novel variant. The attention-based mechanism is seamlessly integrated with the 3D-CU-Net, prioritizing informative features to enhance segmentation accuracy by concentrating on selective regions. This innovative approach serves to significantly improve the model's performance, particularly in challenging cases. The proposed model is evaluated on the TCGA-KIRC dataset, a widely used benchmark for kidney tumor segmentation. In comparative experiments, we evaluated the results using metrics like IoU, DSC and accuracy. Our Attention 3D-CU-Net model outperforms the baseline 3D-CU-Net and U-Net with notably higher scores: IoU (0.92), DSC (0.94), and accuracy (0.96).

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UNet-based Automatic Multicalss Brain Tissue Segmentation

Ahmad,

Chen

2023

2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)

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Brain Tumor Segmentation using 3D U-Net with Hyperparameter Optimization

Cited by 5 publications

References 10 publications

U-Net-Based Models towards Optimal MR Brain Image Segmentation

U-Net-Based Models towards Optimal MR Brain Image Segmentation

Attention 3D-CU-Net: Enhancing Kidney Tumor Segmentation Accuracy Through Selective Feature Emphasis

UNet-based Automatic Multicalss Brain Tissue Segmentation

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